Color appearance models (“CAMs”) have been developed to match colors under different environment conditions that otherwise might be perceived to be different, according to the human visual system (“HVS”). In particular, a color captured (e.g., in an image) under one set of conditions may be perceived as a different color by an observer viewing that color in another set of conditions. The following are examples of factors that can contribute to perceptible color mismatches: the different chromacities and/or luminance levels of different illuminants, different types of devices used to display the color, the relative luminance of the background, different conditions of the surrounding environment, as well as other factors. Conventional color appearance models aim to compensate for these factors by adjusting an image viewed with a destination set of conditions so that it appears to be the same color at which it was captured with a source set of conditions. Thus, color appearance models can be used to convert a patch of color seen in one environment (e.g., the source environment) to an equivalent patch of color as it would be observed in a different environment (e.g., the target environment).
While functional, some approaches in predicting color appearance, including chromatic adaptation, have their drawbacks. In at least one approach, the determination of the effects of different chromacities and luminance levels of illuminants typically requires manual intervention, such as manually measuring environmental parameter values. Often, the environmental parameter values are then encoded as metadata that accompanies the image data for modifying the color at a target environment. Or, in some cases, estimated or presumed values of environmental parameters are used to guide the chromatic adaptation process. To illustrate, consider the CIECAM02 Color Appearance Model (“CAM”) maintained by the International Commission on Illumination (“CIE”) of Vienna, Austria. According to this model, a degree of adaptation, D, is dependent on an adapting luminance, LA, which is typically presumed to be approximately 20% of the luminance of a white object in a viewing environment. The luminance of the white object is typically measured using optical measuring device and the values are manually implemented in predicting color appearance. Further, some color appearance model implementations also derive the degree of adaptation, D, based on a few predetermined, constant surround conditions that relate to a few predetermined values for a luminance level adaptation factor, FL, an impact of surround, c, and a chromatic induction factor, Nc. In some approaches, some parameters are applied globally over most or all portions of an image, regardless whether a portion includes a light source, a reflective surface, or otherwise.
In view of the foregoing, it would be desirable to provide systems, computer-readable media, methods, integrated circuits, and apparatuses to facilitate the prediction of the appearance of color in images for different viewing environments, including high dynamic range images.